RESUMEN
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
RESUMEN
"Big data" has become a major force of innovation across enterprises of all sizes. New platforms with increasingly more features for managing big datasets are being announced almost on a weekly basis. Yet, there is currently a lack of any means of comparability among such platforms. While the performance of traditional database systems is well understood and measured by long-established institutions such as the Transaction Processing Performance Council (TCP), there is neither a clear definition of the performance of big data systems nor a generally agreed upon metric for comparing these systems. In this article, we describe a community-based effort for defining a big data benchmark. Over the past year, a Big Data Benchmarking Community has become established in order to fill this void. The effort focuses on defining an end-to-end application-layer benchmark for measuring the performance of big data applications, with the ability to easily adapt the benchmark specification to evolving challenges in the big data space. This article describes the efforts that have been undertaken thus far toward the definition of a BigData Top100 List. While highlighting the major technical as well as organizational challenges, through this article, we also solicit community input into this process.
RESUMEN
BACKGROUND: Systems that enable remote monitoring of patients' symptoms and other health-related outcomes may optimize cancer care outside of the clinic setting. CYCORE (CYberinfrastructure for COmparative effectiveness REsearch) is a software-based prototype for a user-friendly cyberinfrastructure supporting the comprehensive collection and analyses of data from multiple domains using a suite of home-based and mobile sensors. This study evaluated the feasibility of using CYCORE to address early at-home identification of dehydration risk in head and neck cancer patients undergoing radiation therapy. METHODS: Head and neck cancer patients used home-based sensors to capture weight, blood pressure, pulse, and patient-reported outcomes for two 5-day periods during radiation therapy. Data were sent to the radiation oncologist of each head and neck cancer patient, who viewed them online via a Web-based interface. Feasibility outcomes included study completion rate, acceptability and perceived usefulness of the intervention, and adherence to the monitoring protocol. We also evaluated whether sensor data could identify dehydration-related events. RESULTS: Fifty patients consented to participate, and 48 (96%) completed the study. More than 90% of patients rated their ease, self-efficacy, and satisfaction regarding use of the sensor suite as extremely favorable, with minimal concerns expressed regarding data privacy issues. Patients highly valued the ability to have immediate access to objective, self-monitoring data related to personal risk for dehydration. Clinician assessments indicated a high degree of satisfaction with the ease of using the CYCORE system and the resulting ability to monitor their patients remotely. CONCLUSION: Implementing CYCORE in a clinical oncology care setting is feasible and highly acceptable to both patients and providers.
